Determination of two-phase interfacial areas by an ultrasonic technique

Phase fraction is one of important indexes to characterize multiphase flow. In order to measure each phase fraction of oil-gas-water three-phase flow, liquid level height is detected by Time of Flight – TOF of reflected ultrasound at gas-liquid interface, while oil phase fraction in reflection path is calculated according to the ultrasound attenuation. By studying interactions between multiphase flow and the ultrasound propagation in certain flow patterns, a prediction model for phase fraction measurement of three-phase flow is proposed based on ultrasound transmission attenuation and reflection TOF in the process of horizontal flow with actual phase distributions. Simulation and experimental results under conditions of oil-water two-phase structure with stratified gas in a horizontal pipe show that the proposed method and the established model can accurately detect gas-liquid interface, so that measure oil, gas, water phase fraction. The mechanism prediction model and the measurement device effectively solve the nonlinear response of the ultrasonic measurement parameter, so that can estimate phase fractions of liquids and gas in two-phase as well as three-phase flows simultaneously, which extends the measurement range and the applicable scope of ultrasonic technique to multiphase flow.

Section: Ultrasound Reflection Time-of-flightmentioning

confidence: 99%

“…Schematic of ultrasound transmission attenuation measurement principle is represented in figure 4. Ultrasound attenuation coefficient is expressed as [19]…”

Section: Ultrasound Transmission Attenuationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Phase fraction measurement of oil–gas–water three-phase flow with stratified gas by ultrasound technique

Deng

Liu

et al. 2022

“…The ultrasonic technique based on the principle of pulseecho intensity is widely used in gas/liquid interface detection and often, the location of the interface is determined by measuring the time of flight of the reflected wave (Chang and Morala 1990). Wada et al (2006) presented an ultrasonic method of two-phase flow pattern recognition based on the measurement of the instantaneous echo intensity profile along the ultrasonic beam.…”

Section: Introductionmentioning

confidence: 99%

Non-invasive classification of gas–liquid two-phase horizontal flow regimes using an ultrasonic Doppler sensor and a neural network

Abbagoni¹,

Yeung²

2016

The identification of flow pattern is key issue in multiphase flow which encountered in the petrochemical industry. Gas-liquid two-phase flow is difficult to identify the gas-liquid flow regimes objectively. This paper presents a feasibility of a clamp-on instrument for objective flow regime classification of two-phase flow using an ultrasonic Doppler sensor and artificial neural network. It is on recording and processing of the ultrasonic signals reflected from the two-phase flow. Experimental data obtained on a horizontal test rig with total pipe length of 21 m long and 5.08 cm internal diameter carrying air-water two-phase flow under slug, elongated bubble, stratified-wavy and, stratified flow regimes. Multilayer Perceptron Neural Networks (MLPNNs) used for developing the classification model. The classifier requires features as input which is representative of the signals. Ultrasound signal features extracted by applying both power spectral density (PSD) and discrete wavelet transforms (DWT) methods to the flow signals. A classification scheme of "1-of-C coding method for classification" was adopted to classify features extracted into one of four flow regime categories. To improve the performance of the flow regime classifier network, a second level neural network was incorporated by using output of a first level networks features as input features. Addition of the two network models provided a combined neural network models which has achieved higher accuracy than single neural network models. Classification accuracies evaluated in the form of both the PSD and DWT features. The success rates of the two models are: (1) using PSD features, the classifier missed three datasets out of 24 test datasets of the classification and scored 87.5% accuracy. (2) With the DWT features, the network misclassified only one data point and it was able to classify the flow patterns up to 95.8% accuracy. This approach has demonstrated success of a clamp-on ultrasound sensor for flow regime classification and it would be possible in industry practice. It is considerably more promising than other techniques as it uses of non-invasive and nonradioactive sensor.

“…when humans and animals return from pressurized environments, bubbles occur inside the blood [14][15][16]. Chang and Morla [17] developed methods for determining the size distribution of bubbles in pipes by utilizing a single-pulsed ultrasound transducer. It is worth highlighting that the majority of the studies, generally, have considered a simplistic model of resonance frequency and ignored the effects relating to the elastic properties of bubble walls, stiffness, multi-bubble effects, inertia and the proximity of the boundaries [18].…”

Section: Introductionmentioning

confidence: 99%

A novel ultrasound based technique for classifying gas bubble sizes in liquids

Hussein¹,

Khan²,

Zamorano³

et al. 2014

Characterizing gas bubbles in liquids is crucial to many biomedical, environmental and industrial applications. In this paper a novel method is proposed for the classification of bubble sizes using ultrasound analysis, which is widely acknowledged for being non-invasive, non-contact and inexpensive. This classification is based on 2D templates, i.e. the average spectrum of events representing the trace of bubbles when they cross an ultrasound field. The 2D patterns are obtained by capturing ultrasound signals reflected by bubbles. Frequency-domain based features are analyzed that provide discrimination between bubble sizes. These features are then fed to an artificial neural network, which is designed and trained to classify bubble sizes. The benefits of the proposed method are that it facilitates the processing of multiple bubbles simultaneously, the issues concerning masking interference among bubbles are potentially reduced and using a single sinusoidal component makes the transmitter–receiver electronics relatively simpler. Results from three bubble sizes indicate that the proposed scheme can achieve an accuracy in their classification that is as high as 99%.